The goal of this paper is two-fold:
(1) Towards end-to-end learning of scale-invariant TSP solvers, we unify several state-of-the-art architectures and learning paradigms [1]}, [2]}, [3]}, [4]} into one experimental pipeline and provide the first principled investigation on zero-shot generalization to large instances.
(2) We open-source our framework and datasets to encourage the community to go beyond evaluating performance on fixed TSP sizes and study transfer learning for combinatorial problems.
Our controlled experiments reveal that best practices regarding design choices such as GNN layers, normalization schemes, graph sparsification, and learning paradigms do not hold when evaluating generalization.
In other words, learning to solve TSP at realistic scales will require rethinking the experimental as well as architectural status quo of neural combinatorial optimization to explicitly consider out-of-distribution generalization.
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